Location adds a powerful dimension to data — and some traps of its own.
Types of Spatial Data
- Points: stores, customers, events (latitude and longitude).
- Lines: roads, routes, pipelines.
- Polygons: suburbs, countries, sales territories.
- Rasters: gridded data such as satellite imagery or elevation.
Coordinate Systems
Latitude and longitude are angles on a sphere, not distances. For measuring distances and areas, project data into an appropriate coordinate reference system. Mixing systems silently misplaces data.
Common Operations
- Spatial join: assign points to the polygons they fall within (customers to suburbs).
- Buffers: areas within a distance of a point or line.
- Distance and nearest neighbour: closest store to each customer.
- Aggregation: counts or sums per region.
Mapping Honestly
- Normalise values by population or area in choropleth maps; otherwise large regions dominate.
- Choose sensible colour classes.
- Beware small regions with few observations producing extreme rates.
- Remember that the map's boundaries can shape conclusions (the modifiable areal unit problem).
Tools
GeoPandas and Shapely in Python, QGIS for desktop analysis, PostGIS for spatial SQL. Natural Earth provides free boundary data for world maps.
import geopandas as gpd
countries = gpd.read_file("ne_110m_admin_0_countries.zip")
Privacy
Precise location data can identify people. Aggregate or generalise before sharing.